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ACE agile, contingent and efficient similarity joins using MapReduce
Contents
Abstract
Acknowledgments
Contents
List of Tables
List of Figures
1 Introduction
2 Background
2.1 MapReduce Model
2.2 Hadoop Features
2.3 Multisets and Similarity Measures
2.4 Literature Review
2.4.1 Serial Algorithms
2.4.2 Parallel Algorithms
3 Strategic and Suave Processing for Similarity Joins Using MapRe-duce
3.1 Stage I - Map Phase
3.2 Stage I - Reduce Phase
3.3 Stage II - Map Phase
3.4 Stage II- Reduce Phase
3.4.1 Positional Filtering
3.4.2 Positional Filtering in Stage II-Reduce Phase
3.5 Stage III - Map Phase
3.6 Stage III - Reduce Phase
3.7 Preprocessing
3.8 Comparison of SSS with SSJ-2R
3.9 Experimental Results
3.10 Enhancing the Scalability of the Algorithm
3.11 Summary
4 Adept and Agile Processing for Efficient and Scalable Similarity Joins Using MapReduce
4.1 Stage I - Map Phase
4.2 Stage I - Reduce Phase
4.3 Stage II - Map Phase
4.4 Stage II - Reduce Phase
4.4.1 Suffix Filtering
4.4.2 Optimizing the minimum Prefix Hamming Distance, Hpmin
4.4.3 Suffix Filtering in Stage II- Reduce Phase
4.5 Stage III - Map Phase
4.6 Stage III - Reduce Phase
4.7 Comparison of ESSJ with SSJ-2R
4.8 Experimental Results
4.9 Summary
5 Effcient, Adaptable and Scalable MapReduce Algorithm For Simi-larity Joins Using Hybrid Strategies 74
5.1 Stage II - Reduce Phase
5.2 Stage III - Map Phase
5.3 Stage III - Reduce Phase
5.4 Discussion
6 Conclusion
References Bài viết liên quan
